Overview

Dataset statistics

Number of variables14
Number of observations39607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 4 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_06 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_07 is highly correlated with Y_04 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 8 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_10High correlation
Y_07 is highly correlated with Y_04 and 2 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05High correlation
Y_05 is highly correlated with Y_04High correlation
Y_06 is highly correlated with Y_08 and 6 other fieldsHigh correlation
Y_08 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_09 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_10 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_11 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_12 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_13 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_14 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 4 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_01 and 8 other fieldsHigh correlation
Y_07 is highly correlated with Y_01 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_10 is highly correlated with Y_01 and 10 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 8 other fieldsHigh correlation

Reproduction

Analysis started2022-08-06 00:48:41.726813
Analysis finished2022-08-06 00:49:13.557785
Duration31.83 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Y_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2249
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.353813796
Minimum0.017
Maximum4.409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:13.641720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.7833
Q11.1275
median1.349
Q31.576
95-th percentile1.931
Maximum4.409
Range4.392
Interquartile range (IQR)0.4485

Descriptive statistics

Standard deviation0.3562231101
Coefficient of variation (CV)0.2631256316
Kurtosis1.210970899
Mean1.353813796
Median Absolute Deviation (MAD)0.224
Skewness0.1502434869
Sum53620.503
Variance0.1268949041
MonotonicityNot monotonic
2022-08-06T09:49:13.799333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.37664
 
0.2%
1.31262
 
0.2%
1.33360
 
0.2%
1.4260
 
0.2%
1.38960
 
0.2%
1.27860
 
0.2%
1.360
 
0.2%
1.30859
 
0.1%
1.458
 
0.1%
1.26358
 
0.1%
Other values (2239)39006
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
< 0.1%
0.023
< 0.1%
0.0212
< 0.1%
0.0252
< 0.1%
0.0262
< 0.1%
0.0271
 
< 0.1%
0.0281
 
< 0.1%
0.0351
 
< 0.1%
ValueCountFrequency (%)
4.4091
< 0.1%
4.0811
< 0.1%
3.791
< 0.1%
3.721
< 0.1%
3.5291
< 0.1%
3.5181
< 0.1%
3.5011
< 0.1%
3.4991
< 0.1%
3.4191
< 0.1%
3.3641
< 0.1%

Y_02
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2227
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057267251
Minimum0.007
Maximum3.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:13.964802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.45
Q10.793
median1.044
Q31.3
95-th percentile1.711
Maximum3.998
Range3.991
Interquartile range (IQR)0.507

Descriptive statistics

Standard deviation0.386265985
Coefficient of variation (CV)0.3653437527
Kurtosis0.6736418075
Mean1.057267251
Median Absolute Deviation (MAD)0.254
Skewness0.3657652688
Sum41875.184
Variance0.1492014112
MonotonicityNot monotonic
2022-08-06T09:49:14.127455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07259
 
0.1%
0.81459
 
0.1%
1.1258
 
0.1%
1.04357
 
0.1%
0.92456
 
0.1%
0.88856
 
0.1%
1.03155
 
0.1%
1.14455
 
0.1%
0.90255
 
0.1%
0.83454
 
0.1%
Other values (2217)39043
98.6%
ValueCountFrequency (%)
0.0072
 
< 0.1%
0.0084
< 0.1%
0.0093
< 0.1%
0.014
< 0.1%
0.0113
< 0.1%
0.0123
< 0.1%
0.0135
< 0.1%
0.0142
 
< 0.1%
0.0157
< 0.1%
0.0166
< 0.1%
ValueCountFrequency (%)
3.9981
< 0.1%
3.971
< 0.1%
3.721
< 0.1%
3.5521
< 0.1%
3.2881
< 0.1%
3.2561
< 0.1%
3.2281
< 0.1%
3.1421
< 0.1%
3.1151
< 0.1%
3.0491
< 0.1%

Y_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2127
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.014001717
Minimum0.017
Maximum3.756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:14.298991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.451
Q10.769
median0.998
Q31.239
95-th percentile1.628
Maximum3.756
Range3.739
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3614919509
Coefficient of variation (CV)0.3565003341
Kurtosis0.7764764849
Mean1.014001717
Median Absolute Deviation (MAD)0.235
Skewness0.396399124
Sum40161.566
Variance0.1306764305
MonotonicityNot monotonic
2022-08-06T09:49:14.457694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88866
 
0.2%
0.98863
 
0.2%
0.97362
 
0.2%
0.97161
 
0.2%
0.96561
 
0.2%
1.10460
 
0.2%
0.86960
 
0.2%
0.99959
 
0.1%
0.90657
 
0.1%
0.84657
 
0.1%
Other values (2117)39001
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.0214
< 0.1%
0.0221
 
< 0.1%
0.0242
< 0.1%
0.0252
< 0.1%
0.0272
< 0.1%
0.0293
< 0.1%
0.032
< 0.1%
0.0311
 
< 0.1%
ValueCountFrequency (%)
3.7561
< 0.1%
3.7131
< 0.1%
3.2841
< 0.1%
3.2131
< 0.1%
3.1981
< 0.1%
3.1821
< 0.1%
3.1021
< 0.1%
3.0991
< 0.1%
3.0691
< 0.1%
3.0281
< 0.1%

Y_04
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10773
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62119133
Minimum-0.331
Maximum98.794
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size309.6 KiB
2022-08-06T09:49:14.629101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.331
5-th percentile8.9393
Q111.822
median13.837
Q315.626
95-th percentile17.5587
Maximum98.794
Range99.125
Interquartile range (IQR)3.804

Descriptive statistics

Standard deviation2.686631665
Coefficient of variation (CV)0.1972391107
Kurtosis25.18483477
Mean13.62119133
Median Absolute Deviation (MAD)1.887
Skewness0.4534505598
Sum539494.525
Variance7.217989702
MonotonicityNot monotonic
2022-08-06T09:49:14.795031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.85215
 
< 0.1%
14.24314
 
< 0.1%
13.34914
 
< 0.1%
15.70713
 
< 0.1%
15.46113
 
< 0.1%
13.8913
 
< 0.1%
14.40913
 
< 0.1%
14.77413
 
< 0.1%
15.06313
 
< 0.1%
15.43613
 
< 0.1%
Other values (10763)39473
99.7%
ValueCountFrequency (%)
-0.3311
< 0.1%
-0.3271
< 0.1%
-0.3081
< 0.1%
2.2421
< 0.1%
3.3121
< 0.1%
3.4471
< 0.1%
3.4781
< 0.1%
3.8331
< 0.1%
3.8471
< 0.1%
3.9241
< 0.1%
ValueCountFrequency (%)
98.7941
< 0.1%
33.3331
< 0.1%
25.9561
< 0.1%
21.4621
< 0.1%
21.4421
< 0.1%
20.891
< 0.1%
20.4761
< 0.1%
20.3211
< 0.1%
20.2091
< 0.1%
20.2041
< 0.1%

Y_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10241
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.29046706
Minimum18.589
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:15.081269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.589
5-th percentile26.512
Q129.768
median31.71
Q333.184
95-th percentile34.709
Maximum37.25
Range18.661
Interquartile range (IQR)3.416

Descriptive statistics

Standard deviation2.543221628
Coefficient of variation (CV)0.08127784168
Kurtosis0.489914823
Mean31.29046706
Median Absolute Deviation (MAD)1.658
Skewness-0.7720326285
Sum1239321.529
Variance6.467976249
MonotonicityNot monotonic
2022-08-06T09:49:15.245854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.69218
 
< 0.1%
33.46517
 
< 0.1%
32.49116
 
< 0.1%
31.94915
 
< 0.1%
33.21515
 
< 0.1%
31.71315
 
< 0.1%
32.65915
 
< 0.1%
32.59315
 
< 0.1%
32.715
 
< 0.1%
32.79615
 
< 0.1%
Other values (10231)39451
99.6%
ValueCountFrequency (%)
18.5891
< 0.1%
19.3951
< 0.1%
19.7041
< 0.1%
20.0621
< 0.1%
20.0672
< 0.1%
20.1231
< 0.1%
20.1891
< 0.1%
20.241
< 0.1%
20.4171
< 0.1%
20.4621
< 0.1%
ValueCountFrequency (%)
37.251
< 0.1%
37.2251
< 0.1%
37.1012
< 0.1%
36.9951
< 0.1%
36.9791
< 0.1%
36.9151
< 0.1%
36.8681
< 0.1%
36.8371
< 0.1%
36.8081
< 0.1%
36.8061
< 0.1%

Y_06
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4269
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52938208
Minimum-19.963
Maximum18.998
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size309.6 KiB
2022-08-06T09:49:15.415408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.963
5-th percentile15.177
Q116.146
median16.694
Q317.164
95-th percentile17.758
Maximum18.998
Range38.961
Interquartile range (IQR)1.018

Descriptive statistics

Standard deviation1.89301384
Coefficient of variation (CV)0.1145241747
Kurtosis270.339787
Mean16.52938208
Median Absolute Deviation (MAD)0.501
Skewness-15.02970347
Sum654679.236
Variance3.583501399
MonotonicityNot monotonic
2022-08-06T09:49:15.576481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78238
 
0.1%
16.87236
 
0.1%
17.09933
 
0.1%
16.85932
 
0.1%
17.13832
 
0.1%
16.96732
 
0.1%
16.7232
 
0.1%
16.84731
 
0.1%
16.7631
 
0.1%
16.47531
 
0.1%
Other values (4259)39279
99.2%
ValueCountFrequency (%)
-19.9631
< 0.1%
-19.6021
< 0.1%
-19.5171
< 0.1%
-19.4721
< 0.1%
-19.4431
< 0.1%
-19.3671
< 0.1%
-19.3511
< 0.1%
-19.2521
< 0.1%
-19.232
< 0.1%
-19.0991
< 0.1%
ValueCountFrequency (%)
18.9981
< 0.1%
18.9921
< 0.1%
18.8881
< 0.1%
18.8571
< 0.1%
18.8241
< 0.1%
18.7861
< 0.1%
18.7531
< 0.1%
18.7281
< 0.1%
18.6921
< 0.1%
18.6851
< 0.1%

Y_07
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2394
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155054107
Minimum0.502
Maximum5.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:15.743418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.502
5-th percentile2.53
Q12.863
median3.126
Q33.4335
95-th percentile3.864
Maximum5.299
Range4.797
Interquartile range (IQR)0.5705

Descriptive statistics

Standard deviation0.4189399013
Coefficient of variation (CV)0.1327837454
Kurtosis0.7671083874
Mean3.155054107
Median Absolute Deviation (MAD)0.283
Skewness0.08450194006
Sum124962.228
Variance0.1755106409
MonotonicityNot monotonic
2022-08-06T09:49:15.902103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.00557
 
0.1%
2.9456
 
0.1%
3.13653
 
0.1%
2.98852
 
0.1%
3.12852
 
0.1%
3.03351
 
0.1%
3.27251
 
0.1%
2.97651
 
0.1%
3.0351
 
0.1%
3.0850
 
0.1%
Other values (2384)39083
98.7%
ValueCountFrequency (%)
0.5021
< 0.1%
0.6851
< 0.1%
0.7231
< 0.1%
0.8181
< 0.1%
0.8791
< 0.1%
0.9111
< 0.1%
0.9211
< 0.1%
0.9331
< 0.1%
0.9451
< 0.1%
0.9531
< 0.1%
ValueCountFrequency (%)
5.2991
< 0.1%
5.1181
< 0.1%
4.9991
< 0.1%
4.9911
< 0.1%
4.9821
< 0.1%
4.9271
< 0.1%
4.9181
< 0.1%
4.911
< 0.1%
4.8681
< 0.1%
4.851
< 0.1%

Y_08
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3672
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.29483879
Minimum-29.652
Maximum-23.785
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:16.060194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.652
5-th percentile-27.4447
Q1-26.689
median-26.254
Q3-25.855
95-th percentile-25.283
Maximum-23.785
Range5.867
Interquartile range (IQR)0.834

Descriptive statistics

Standard deviation0.6605368289
Coefficient of variation (CV)-0.0251203985
Kurtosis0.7493218708
Mean-26.29483879
Median Absolute Deviation (MAD)0.415
Skewness-0.4373902743
Sum-1041459.68
Variance0.4363089024
MonotonicityNot monotonic
2022-08-06T09:49:16.212665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.31441
 
0.1%
-26.09140
 
0.1%
-25.83840
 
0.1%
-26.43139
 
0.1%
-26.43539
 
0.1%
-26.0939
 
0.1%
-26.08138
 
0.1%
-26.37238
 
0.1%
-26.04537
 
0.1%
-26.12236
 
0.1%
Other values (3662)39220
99.0%
ValueCountFrequency (%)
-29.6521
< 0.1%
-29.6421
< 0.1%
-29.6051
< 0.1%
-29.5781
< 0.1%
-29.4521
< 0.1%
-29.3521
< 0.1%
-29.331
< 0.1%
-29.3241
< 0.1%
-29.3092
< 0.1%
-29.3061
< 0.1%
ValueCountFrequency (%)
-23.7851
< 0.1%
-24.0131
< 0.1%
-24.1171
< 0.1%
-24.1421
< 0.1%
-24.1581
< 0.1%
-24.1621
< 0.1%
-24.181
< 0.1%
-24.191
< 0.1%
-24.2071
< 0.1%
-24.2111
< 0.1%

Y_09
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.30862254
Minimum-29.523
Maximum-23.96
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:16.375853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.523
5-th percentile-27.44
Q1-26.702
median-26.266
Q3-25.871
95-th percentile-25.311
Maximum-23.96
Range5.563
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6535798156
Coefficient of variation (CV)-0.02484279877
Kurtosis0.7264309573
Mean-26.30862254
Median Absolute Deviation (MAD)0.414
Skewness-0.4318247115
Sum-1042005.613
Variance0.4271665753
MonotonicityNot monotonic
2022-08-06T09:49:16.526808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.3143
 
0.1%
-26.22841
 
0.1%
-26.2838
 
0.1%
-26.10638
 
0.1%
-26.26537
 
0.1%
-26.12337
 
0.1%
-26.0437
 
0.1%
-26.37537
 
0.1%
-26.1137
 
0.1%
-26.33136
 
0.1%
Other values (3639)39226
99.0%
ValueCountFrequency (%)
-29.5231
< 0.1%
-29.4771
< 0.1%
-29.471
< 0.1%
-29.4271
< 0.1%
-29.3921
< 0.1%
-29.3761
< 0.1%
-29.3511
< 0.1%
-29.3391
< 0.1%
-29.3381
< 0.1%
-29.3311
< 0.1%
ValueCountFrequency (%)
-23.961
< 0.1%
-23.9851
< 0.1%
-24.0911
< 0.1%
-24.1041
< 0.1%
-24.1551
< 0.1%
-24.161
< 0.1%
-24.1891
< 0.1%
-24.2191
< 0.1%
-24.2421
< 0.1%
-24.2761
< 0.1%

Y_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4458
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.40006244
Minimum-31.119
Maximum-20.052
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:16.692737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-31.119
5-th percentile-23.9517
Q1-22.871
median-22.275
Q3-21.791
95-th percentile-21.195
Maximum-20.052
Range11.067
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.920952195
Coefficient of variation (CV)-0.04111382268
Kurtosis10.34745855
Mean-22.40006244
Median Absolute Deviation (MAD)0.529
Skewness-1.837054602
Sum-887199.273
Variance0.8481529455
MonotonicityNot monotonic
2022-08-06T09:49:16.847224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.01433
 
0.1%
-21.99533
 
0.1%
-21.91932
 
0.1%
-22.09632
 
0.1%
-22.32732
 
0.1%
-22.17631
 
0.1%
-22.09231
 
0.1%
-22.32931
 
0.1%
-22.06531
 
0.1%
-21.78630
 
0.1%
Other values (4448)39291
99.2%
ValueCountFrequency (%)
-31.1191
< 0.1%
-30.9491
< 0.1%
-30.9261
< 0.1%
-30.7881
< 0.1%
-30.6191
< 0.1%
-30.5871
< 0.1%
-30.5841
< 0.1%
-30.5481
< 0.1%
-30.5371
< 0.1%
-30.5071
< 0.1%
ValueCountFrequency (%)
-20.0521
 
< 0.1%
-20.0931
 
< 0.1%
-20.131
 
< 0.1%
-20.1471
 
< 0.1%
-20.2241
 
< 0.1%
-20.2351
 
< 0.1%
-20.2721
 
< 0.1%
-20.2883
< 0.1%
-20.311
 
< 0.1%
-20.3311
 
< 0.1%

Y_11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4309
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.32506113
Minimum19.844
Maximum26.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T09:49:17.006107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.844
5-th percentile22.815
Q123.836
median24.42
Q324.9115
95-th percentile25.514
Maximum26.703
Range6.859
Interquartile range (IQR)1.0755

Descriptive statistics

Standard deviation0.8301968024
Coefficient of variation (CV)0.03412927919
Kurtosis0.7579205164
Mean24.32506113
Median Absolute Deviation (MAD)0.532
Skewness-0.6749349242
Sum963442.696
Variance0.6892267307
MonotonicityNot monotonic
2022-08-06T09:49:17.161987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.73734
 
0.1%
24.77634
 
0.1%
24.49633
 
0.1%
24.5832
 
0.1%
24.54232
 
0.1%
24.40932
 
0.1%
24.64432
 
0.1%
24.50931
 
0.1%
24.74131
 
0.1%
24.58831
 
0.1%
Other values (4299)39285
99.2%
ValueCountFrequency (%)
19.8441
< 0.1%
20.0311
< 0.1%
20.0451
< 0.1%
20.1011
< 0.1%
20.1751
< 0.1%
20.1941
< 0.1%
20.1991
< 0.1%
20.2951
< 0.1%
20.2981
< 0.1%
20.3341
< 0.1%
ValueCountFrequency (%)
26.7031
< 0.1%
26.6591
< 0.1%
26.6571
< 0.1%
26.5921
< 0.1%
26.5791
< 0.1%
26.5671
< 0.1%
26.5511
< 0.1%
26.5451
< 0.1%
26.4831
< 0.1%
26.481
< 0.1%

Y_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3673
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23776173
Minimum-29.544
Maximum-23.722
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:17.326588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.544
5-th percentile-27.38
Q1-26.63
median-26.198
Q3-25.799
95-th percentile-25.238
Maximum-23.722
Range5.822
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6563285123
Coefficient of variation (CV)-0.02501465327
Kurtosis0.7459825
Mean-26.23776173
Median Absolute Deviation (MAD)0.413
Skewness-0.4446574078
Sum-1039199.029
Variance0.430767116
MonotonicityNot monotonic
2022-08-06T09:49:17.479209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.02649
 
0.1%
-26.1741
 
0.1%
-26.07640
 
0.1%
-26.35839
 
0.1%
-26.11839
 
0.1%
-26.4438
 
0.1%
-26.29238
 
0.1%
-25.99837
 
0.1%
-25.91937
 
0.1%
-26.35437
 
0.1%
Other values (3663)39212
99.0%
ValueCountFrequency (%)
-29.5441
< 0.1%
-29.4531
< 0.1%
-29.4411
< 0.1%
-29.3671
< 0.1%
-29.3461
< 0.1%
-29.3411
< 0.1%
-29.3351
< 0.1%
-29.311
< 0.1%
-29.2871
< 0.1%
-29.2831
< 0.1%
ValueCountFrequency (%)
-23.7221
< 0.1%
-23.9471
< 0.1%
-23.951
< 0.1%
-24.0671
< 0.1%
-24.1511
< 0.1%
-24.161
< 0.1%
-24.2211
< 0.1%
-24.2281
< 0.1%
-24.2311
< 0.1%
-24.241
< 0.1%

Y_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3665
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23386934
Minimum-29.448
Maximum-23.899
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:17.643260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.448
5-th percentile-27.366
Q1-26.624
median-26.193
Q3-25.794
95-th percentile-25.2393
Maximum-23.899
Range5.549
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.6550900257
Coefficient of variation (CV)-0.02497115531
Kurtosis0.7518019689
Mean-26.23386934
Median Absolute Deviation (MAD)0.413
Skewness-0.4398630698
Sum-1039044.863
Variance0.4291429417
MonotonicityNot monotonic
2022-08-06T09:49:17.922057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.09742
 
0.1%
-26.18741
 
0.1%
-26.33641
 
0.1%
-26.2840
 
0.1%
-26.15139
 
0.1%
-26.34638
 
0.1%
-26.03938
 
0.1%
-25.96837
 
0.1%
-26.21336
 
0.1%
-25.99536
 
0.1%
Other values (3655)39219
99.0%
ValueCountFrequency (%)
-29.4481
< 0.1%
-29.4431
< 0.1%
-29.3751
< 0.1%
-29.3681
< 0.1%
-29.3551
< 0.1%
-29.351
< 0.1%
-29.3011
< 0.1%
-29.2921
< 0.1%
-29.2361
< 0.1%
-29.2261
< 0.1%
ValueCountFrequency (%)
-23.8991
< 0.1%
-23.9361
< 0.1%
-23.9651
< 0.1%
-24.0211
< 0.1%
-24.1171
< 0.1%
-24.1231
< 0.1%
-24.1771
< 0.1%
-24.1941
< 0.1%
-24.2051
< 0.1%
-24.211
< 0.1%

Y_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3682
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.24586843
Minimum-29.62
Maximum-23.856
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T09:49:18.082667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.62
5-th percentile-27.3817
Q1-26.64
median-26.204
Q3-25.809
95-th percentile-25.245
Maximum-23.856
Range5.764
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6559887312
Coefficient of variation (CV)-0.02499398078
Kurtosis0.734812393
Mean-26.24586843
Median Absolute Deviation (MAD)0.413
Skewness-0.4307872388
Sum-1039520.111
Variance0.4303212155
MonotonicityNot monotonic
2022-08-06T09:49:18.234261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.30346
 
0.1%
-26.02541
 
0.1%
-26.14839
 
0.1%
-25.85839
 
0.1%
-26.0839
 
0.1%
-26.17438
 
0.1%
-26.10538
 
0.1%
-25.83538
 
0.1%
-26.42437
 
0.1%
-26.06537
 
0.1%
Other values (3672)39215
99.0%
ValueCountFrequency (%)
-29.621
< 0.1%
-29.5291
< 0.1%
-29.4931
< 0.1%
-29.4341
< 0.1%
-29.341
< 0.1%
-29.3351
< 0.1%
-29.3121
< 0.1%
-29.2921
< 0.1%
-29.2821
< 0.1%
-29.281
< 0.1%
ValueCountFrequency (%)
-23.8561
< 0.1%
-24.0521
< 0.1%
-24.1372
< 0.1%
-24.1391
< 0.1%
-24.1651
< 0.1%
-24.1761
< 0.1%
-24.1921
< 0.1%
-24.1931
< 0.1%
-24.2081
< 0.1%
-24.2111
< 0.1%

Interactions

2022-08-06T09:49:11.020563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.329127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.151904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.275869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.239040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.225422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.189504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.354515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.211195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.256622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.169297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.185702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.128387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.130485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.147666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.449616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.285967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.405552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.364626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.356073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.322148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.480115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.339620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.385277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.295986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.316512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.255048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.255978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.291422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.586856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.438499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.553128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.632522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.503679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.600295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.620763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.613845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.532913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.439599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.465590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.396856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.398189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.431314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.722761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.709370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.696927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.771822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.648538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.746398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.757743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.758484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.675502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.707881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.610229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.663383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.537791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.687628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.848453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.846659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.841482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.900507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.783970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.889526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.884000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.889230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.807149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.836690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.744843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.791932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.666476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.825488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:45.983895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.995351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.987092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.038109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.930919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.035003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.026231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.034816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.948526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.977320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.890200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:07.931402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.808130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:11.967495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.121712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.145264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.136693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.178733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.078387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.185576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.165440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.180456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.092117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.119936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.037351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.070992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:09.949614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.094190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.244414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.280650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.270336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.304259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.211019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.320217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.289611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.309785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.230771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.247594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.168965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.198626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.077249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.226425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.372638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.422908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.409129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.433942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.350544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.465827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.420632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.443877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.363666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.381259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.305569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.330510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.213156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.357273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.502230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.562968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.547781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.565803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.489166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.621412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.550310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.579513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.496389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.514018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.441872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.463255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.348793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.489671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.629869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.704114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.684639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.695457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.628032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.763033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.680924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.713569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.630195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.646939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.577509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.593907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.483458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.628105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.763027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.855794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.827261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.833113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.773356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:56.910663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.816456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.854193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.768824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.787593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.719215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.734559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.623059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.760727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:46.892611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:48.995159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:50.965141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:52.964318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:54.911985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.054253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:58.947464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:00.987179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:02.903463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:04.920208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.855717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.866206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.755730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:12.892375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:47.021255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:49.135759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:51.101432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:53.094942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:55.051588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:57.194877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:48:59.080814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:01.122785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:03.036418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:05.053850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:06.992706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:08.997864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T09:49:10.888381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-06T09:49:18.382144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-06T09:49:18.586633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-06T09:49:18.790053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-06T09:49:18.992557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-06T09:49:13.096105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-06T09:49:13.400314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
02.0561.4561.68010.50229.63216.0834.276-25.381-25.529-22.76923.792-25.470-25.409-25.304
11.4461.1841.26818.50733.17916.7363.229-26.619-26.523-22.57424.691-26.253-26.497-26.438
21.2510.6650.78214.08231.80117.0802.839-26.238-26.216-22.16924.649-26.285-26.215-26.370
31.4641.0791.05216.97534.50317.1433.144-25.426-25.079-21.76524.913-25.254-25.021-25.345
40.9830.6460.68915.04732.60217.5693.138-25.376-25.242-21.07225.299-25.072-25.195-24.974
51.1550.6780.58011.76029.66216.2013.343-26.466-26.527-22.62124.064-26.489-26.536-26.426
62.1401.4381.68914.13732.73915.5732.418-27.581-28.038-23.35523.051-27.650-27.709-27.599
71.7691.5351.53417.95434.68018.2303.147-24.917-24.832-20.68926.138-24.539-24.538-24.668
81.3260.9450.88313.95229.12916.6083.931-25.890-25.801-22.52124.353-25.738-25.825-25.764
92.0041.7871.54816.88534.20918.1202.646-25.520-25.408-21.15925.961-25.353-25.567-25.470

Last rows

Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
395971.4891.3691.30315.68734.08917.5863.107-25.927-25.836-21.61125.399-25.850-25.867-25.587
395981.2990.6121.03217.95732.87016.8043.140-26.569-26.304-23.10224.660-26.259-26.410-26.365
395990.9490.8910.76717.70630.87717.0902.547-26.652-26.807-22.18824.737-26.783-26.694-26.771
396000.9980.5630.9118.87928.95716.4413.387-26.545-26.572-22.70524.084-26.618-26.677-26.530
396011.5561.4181.32812.59832.67116.9492.996-26.106-26.281-22.35924.661-26.134-26.300-26.306
396021.3821.2151.26310.87429.19416.5823.410-26.486-26.581-22.77224.261-26.491-26.584-26.580
396031.4820.6061.0838.75929.85915.6593.406-27.308-27.203-24.67423.427-27.250-27.334-27.325
396041.1171.1540.99313.15924.72016.8233.215-26.502-26.687-22.57724.301-26.388-26.425-26.601
396050.8950.1870.4779.12326.41215.7574.216-26.760-26.634-24.06623.305-26.536-26.751-26.635
396061.1470.3480.85210.42130.74516.7813.307-26.054-26.251-23.25724.450-26.224-26.256-26.093